Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "165" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 18 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 18 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459996 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 27.550027 | 0.544015 | 0.510740 | -0.431279 | 1.886681 | 0.189886 | 0.301862 | -0.361664 | 0.5008 | 0.6587 | 0.3734 | nan | nan |
| 2459994 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 24.367538 | 0.482794 | -0.569688 | -0.552708 | 3.427665 | 0.278905 | 9.857414 | 0.750313 | 0.5294 | 0.6524 | 0.3669 | nan | nan |
| 2459991 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 28.125870 | 0.302073 | -0.620897 | -0.727606 | 4.854178 | 0.078786 | 18.491499 | 0.258153 | 0.5367 | 0.6499 | 0.3760 | nan | nan |
| 2459990 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 25.689787 | 0.191796 | -0.547984 | -0.798088 | 4.583269 | 0.105116 | 25.087740 | 0.236896 | 0.5290 | 0.6504 | 0.3761 | nan | nan |
| 2459989 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 26.023100 | -0.185078 | -0.417979 | -0.467628 | 4.497410 | 0.316611 | 18.597864 | 0.120220 | 0.5243 | 0.6499 | 0.3736 | nan | nan |
| 2459988 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 32.472630 | 0.154113 | -0.588870 | -0.905136 | 5.983121 | -0.219315 | 20.616021 | 0.120033 | 0.5230 | 0.6525 | 0.3666 | nan | nan |
| 2459987 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 29.172890 | 0.458571 | -0.438860 | -0.594763 | 2.229127 | 0.138075 | 2.447918 | -0.421763 | 0.5163 | 0.6544 | 0.3646 | nan | nan |
| 2459986 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 35.006187 | 0.505575 | -0.493026 | -1.078706 | 4.216021 | -0.111947 | 4.110912 | -0.735753 | 0.5407 | 0.6820 | 0.3279 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 33.284031 | 0.510791 | -0.397538 | -0.911344 | 4.185455 | 0.094667 | 7.316796 | -0.224908 | 0.5089 | 0.6576 | 0.3675 | nan | nan |
| 2459984 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 29.525694 | 0.515110 | -0.187638 | -0.996280 | 3.059809 | -0.200185 | 0.311672 | -0.751772 | 0.5261 | 0.6737 | 0.3427 | nan | nan |
| 2459983 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 29.782234 | 0.080332 | -0.447694 | -0.878538 | 5.799712 | -0.171978 | 7.127782 | -0.978581 | 0.5477 | 0.6889 | 0.3164 | nan | nan |
| 2459982 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 16.268473 | 0.829108 | -0.532815 | -0.525762 | 3.043434 | 0.330034 | 1.174951 | -0.195637 | 0.6477 | 0.7255 | 0.2770 | nan | nan |
| 2459981 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 29.127688 | -0.153740 | -0.517482 | -1.104475 | 5.138636 | -0.298065 | 6.493139 | 0.274153 | 0.5167 | 0.6576 | 0.3705 | nan | nan |
| 2459980 | digital_ok | 100.00% | 98.38% | 98.33% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.4463 | 0.4891 | 0.2639 | nan | nan |
| 2459979 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 30.818602 | -0.256372 | -0.608020 | -0.910949 | 3.501991 | 0.037801 | 3.878977 | 0.239087 | 0.5081 | 0.6529 | 0.3728 | nan | nan |
| 2459978 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 31.970585 | -0.247210 | -0.537332 | -1.014421 | 3.838442 | -0.045362 | 1.974523 | -0.397249 | 0.5004 | 0.6520 | 0.3760 | nan | nan |
| 2459977 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 30.412685 | 0.060935 | -0.513717 | -0.891293 | 4.321117 | -0.137091 | 2.465203 | -0.176531 | 0.4698 | 0.6184 | 0.3422 | nan | nan |
| 2459976 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 31.544837 | -0.098676 | -0.527270 | -1.009467 | 3.509481 | -0.097986 | 2.124520 | 0.249936 | 0.5067 | 0.6600 | 0.3622 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 27.550027 | 27.550027 | 0.544015 | 0.510740 | -0.431279 | 1.886681 | 0.189886 | 0.301862 | -0.361664 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 24.367538 | 24.367538 | 0.482794 | -0.569688 | -0.552708 | 3.427665 | 0.278905 | 9.857414 | 0.750313 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 28.125870 | 28.125870 | 0.302073 | -0.620897 | -0.727606 | 4.854178 | 0.078786 | 18.491499 | 0.258153 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 25.689787 | 0.191796 | 25.689787 | -0.798088 | -0.547984 | 0.105116 | 4.583269 | 0.236896 | 25.087740 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 26.023100 | -0.185078 | 26.023100 | -0.467628 | -0.417979 | 0.316611 | 4.497410 | 0.120220 | 18.597864 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 32.472630 | 0.154113 | 32.472630 | -0.905136 | -0.588870 | -0.219315 | 5.983121 | 0.120033 | 20.616021 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 29.172890 | 29.172890 | 0.458571 | -0.438860 | -0.594763 | 2.229127 | 0.138075 | 2.447918 | -0.421763 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 35.006187 | 0.505575 | 35.006187 | -1.078706 | -0.493026 | -0.111947 | 4.216021 | -0.735753 | 4.110912 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 33.284031 | 0.510791 | 33.284031 | -0.911344 | -0.397538 | 0.094667 | 4.185455 | -0.224908 | 7.316796 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 29.525694 | 29.525694 | 0.515110 | -0.187638 | -0.996280 | 3.059809 | -0.200185 | 0.311672 | -0.751772 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 29.782234 | 29.782234 | 0.080332 | -0.447694 | -0.878538 | 5.799712 | -0.171978 | 7.127782 | -0.978581 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 16.268473 | 16.268473 | 0.829108 | -0.532815 | -0.525762 | 3.043434 | 0.330034 | 1.174951 | -0.195637 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 29.127688 | -0.153740 | 29.127688 | -1.104475 | -0.517482 | -0.298065 | 5.138636 | 0.274153 | 6.493139 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 30.818602 | 30.818602 | -0.256372 | -0.608020 | -0.910949 | 3.501991 | 0.037801 | 3.878977 | 0.239087 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 31.970585 | -0.247210 | 31.970585 | -1.014421 | -0.537332 | -0.045362 | 3.838442 | -0.397249 | 1.974523 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 30.412685 | 30.412685 | 0.060935 | -0.513717 | -0.891293 | 4.321117 | -0.137091 | 2.465203 | -0.176531 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | N14 | digital_ok | ee Shape | 31.544837 | -0.098676 | 31.544837 | -1.009467 | -0.527270 | -0.097986 | 3.509481 | 0.249936 | 2.124520 |